Summary: We hypothesize that skin microbiota (bacteria, fungi, viruses, phage, archae) plays a role in common dermatological conditions, such as atopic dermatitis (eczema). There are two classical explanations for how microbes might affect skin disease: (1) a specific microbe colonizes the skin to disrupt the balance of commensal microflora, or (2) a specific microbe releases toxic substances or invade cells to induce an inflammatory response directly. However, it is increasingly clear that bacteria flourish and compete within a larger microbial community. My laboratory developed clinical and laboratory methodologies to characterize skin microbial communities with genomic techniques, which offer significant advantages over traditional culture-based studies. We performed the first skin microbiome survey, characterizing the diversity of microbes that live on normal adult volunteers, and determined that humans are ecosystems with niche-dependent bacterial populations characterized at the highest level as dry, moist or oily regions (Grice et al, Science 2009). All of our human skin microbiome work is carried out under clinicaltrials.gov NCT00605878; PI: Segre. Together with clinical collaborators, we are extending these studies to pediatric patient populations with common atopic dermatitis (AD, eczema; OMIM 603165). We focus our studies on AD because these patients typically respond to various antimicrobial therapies, but there are no biomarkers to direct an individual patients treatment. Atopic dermatitis (AD) has long been associated with Staphylococcus aureus skin colonization or infection. However, the role of microbial communities in the pathogenesis of AD is incompletely characterized. To assess the relationship between skin microbiota and disease progression, 16S ribosomal RNA bacterial gene sequencing was performed on DNA obtained directly from serial skin sampling of children with AD. The composition of bacterial communities was analyzed during AD disease states to identify characteristics associated with AD flares and improvement post-treatment. We found that microbial community structures at sites of disease predilection were dramatically different in AD patients compared with controls. Microbial diversity during AD flares was dependent on presence or absence of recent AD treatments, with even intermittent treatment linked to greater bacterial diversity than no recent treatment. Treatment-associated changes in skin bacterial diversity suggest that AD treatments diversify skin bacteria preceding improvements in disease activity. In AD, the proportion of Staphylococcus sequences, particularly S. aureus, was greater during disease flares than at baseline or post-treatment and correlated with worsened disease severity. Representation of the skin commensal S. epidermidis also significantly increased during flares. Increases in Streptococcus, Propionibacterium, and Corynebacterium species were observed following therapy. These findings reveal linkages between microbial communities and inflammatory diseases such as AD, and demonstrate that, as compared to culture-based studies, higher resolution examination of microbiota associated with human disease provides novel insights into global shifts of bacteria relevant to disease progression and treatment. Our current efforts are directed at examining the skin microbiome as an early predictor of AD flare with dense longitudinal sampling. As well, we have initiated collaborations to perform microbial sequencing and analysis of three birth cohorts to examine onset of AD in the first year of life. Mechanistically, we are assessing the skin microbiomes role in driving AD with animal models recapitulating the skin disorder. Future microbiome studies will integrate genetics of both host (human) and microbes, realizing that we are superorganisms with trillions of microbes living in and on our bodies and integratinng gene-environment interactions. These studies require a major transition from survey-based studies of microbes to full metagenomic sequencing. Metagenomic computational tools and algorithms are still in their infancy and primarily tuned to gut microbial communities. To build the resources needed for skin microbiome analysis, we sequenced reference skin microbes, such as the commensal Staphylococcus epidermidis. We have determined that the S. epidermidis pan-genome is quite large with 80% core genes (2,000 total) and the remaining 20% of genes selected from a large pool of 5000 genes. We are comparing these skin commensal genomes with pathogenic S. epidermidis, isolated from central line catheters and other indwelling medical devices, to develop better diagnostic markers and to explore the transition from commensal to pathogen.